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Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are reduction of systematic biases primarily introduced through technical sources and transformation of counts to make them more amenable for the application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo. [ABSTRACT FROM AUTHOR]

We report on the crystal growth ofthe quasi-one-dimensional quantum spin chain compound SrCuO2and its doped variants containing magnetic cobalt (0.25%, 0.5%,1%, and 2.5%) and nonmagnetic zinc (0.5% and 1%) impurities. Crystalsare grown using the traveling solvent floating zone (TSFZ) methodin a four-mirror optical furnace. Some crucial factors, key to thestability of a TSFZ process, including the choice of solvent compositionand its associated melting behavior, are discussed in detail. Thegrown crystals were characterized using X-ray powder diffraction,scanning electron microscopy, optical microscopy, neutron single crystaldiffraction and magnetic susceptibility measurements. Co-doping inducesmagnetic anisotropy and a corresponding change of magnetic behaviorcharacterized by the presence of a sharp peak in the magnetic susceptibilityat low-temperatures (T= 5 K), which is not seenfor the pristine compound. The peak position is shown to scale linearlywith Co-concentration for low doping levels up to 2.5%. [ABSTRACT FROM AUTHOR]

The ribosome is an ancient machine, performing the same function across organisms. Although functionally unitary, recent experiments suggest specialized roles for some ribosomal proteins. Our central thesis is that ribosomal proteins function in a modular fashion to decode genetic information in a context dependent manner. We show through large data analyses that although many ribosomal proteins are essential with consistent effect on growth in different conditions in yeast and similar expression across cell and tissue types in mice and humans, some ribosomal proteins are used in an environment specific manner. The latter set of variable ribosomal proteins further function in a coordinated manner forming modules, which are adapted to different environmental cues in different organisms. We show that these environment specific modules of ribosomal proteins in yeast have differential genetic interactions with other pathways and their 5’UTRs show differential signatures of selection in yeast strains, presumably to facilitate adaptation. Similarly, we show that in higher metazoans such as mice and humans, different modules of ribosomal proteins are expressed in different cell types and tissues. A clear example is nervous tissue that uses a ribosomal protein module distinct from the rest of the tissues in both mice and humans. Our results suggest a novel stratification of ribosomal proteins that could have played a role in adaptation, presumably to optimize translation for adaptation to diverse ecological niches and tissue microenvironments. [ABSTRACT FROM AUTHOR]